hskl 0.0.2.dev1

Hyperspectral image analysis with scikit-learn.

HSKL: Hyperspectral-scikit-learn

Hyperspectral image analysis using scikit-learn

Installation

The package can be installed from pip:

pip install hskl

Usage

Training a pixel-level classifier for segmentation:

import hskl.classification as classification
import hskl.utils as utils

# List method names
methods = classification.list_methods()

# Load training, testing, and label images (numpy.ndarray)
train, test, label = ...

# Dimensional reduction using PCA, retain 80% image variance
pca = utils.pca_fit(train)
train = utils.pca_apply(train, pca, 0.8)
test = utils.pca_apply(test, pca, 0.8)

# Train a classifier and predict test image labels
cl = classification.HyperspectralClassifier(
         method_name=”RandomForest”,
         method_params={"max_depth": 2})
cl.fit(train, label)
prediction = cl.predict(test)

# Visualization of classification result overlaid with original image
fig_objs = utils.overlay(test,prediction)

Notes:

1. Shape of train and test arrays are (DimX, DimY, SpectralChannels).
2. Shape of label and prediction arrays are (DimX, DimY).
3. Labeling convention for classifiers: (a) Datatype: label.dtype == np.uint8. (b) Labeled classes start from integer 1. Pixels with label == 0 are ignored (masked out).
4. Dimension(s) of train and label must be consistent: train.shape[0] == label.shape[0] and train.shape[1] == label.shape[1].
5. Inputs: train, test, and label can also be lists of np.ndarrays with each element satisfying the preceeding requirements.

Planned Features

In the near-term:

• Test scripts and data
• Grid search cross validation

In the long-term, support for:

• Pipelines
• Patch-based featurizer
• Dask-enabled parallelism
• Deep learning (PyTorch) models

Acknowledgement

Some functionalities in this package are provided by Spectral Python (SPy): https://github.com/spectralpython/spectral